OpenRaiser/Intern-Atlas huggingface dataset:
The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

Deep artificial neural networks have made remarkable progress in different tasks in the field of computer vision. However, the empirical analysis of these models and investigation of their failure cases has received attention recently. In this work, we show that deep learning models cannot generalize to atypical images that are substantially different from training images. This is in contrast to the superior generalization ability of the visual system in the human brain. We focus on Convolutional Neural Networks (CNN) as the state-of-the-art models in object recognition and classification; investigate this problem in more detail, and hypothesize that training CNN models suffer from unstructured loss minimization. We propose computational models to improve the generalization capacity of CNNs by considering how typical a training image looks like. By conducting an extensive set of experiments we show that involving a typicality measure can improve the classification results on a new set of images by a large margin. More importantly, this significant improvement is achieved without fine-tuning the CNN model on the target image set.
Fast phase retrieval for high dimensions: A block-based approach
Venue: arXiv cs.IT 2016
Year: 2016

This paper addresses fundamental scaling issues that hinder phase retrieval (PR) in high dimensions. We show that, if the measurement matrix can be put into a generalized block-diagonal form, a large PR problem can be solved on separate blocks, at the cost of a few extra global measurements to merge the partial results. We illustrate this principle using two distinct PR methods, and discuss different design trade-offs. Experimental results indicate that this block-based PR framework can reduce computational cost and memory requirements by several orders of magnitude.
OpenRISC System-on-Chip Design Emulation
Venue: arXiv cs.AR 2016
Year: 2016

Recently the hardware emulation technique has emerged as a promising approach to accelerating hardware verification/debugging process. To fully evaluate the powerfulness of the emulation approach and demonstrate its potential impact, we propose to emulate a system-on-chip (SoC) design using Mentor Graphics Veloce emulation platform. This article presents our project setup and the results we have achieved. The results are encouraging. ORPSoC emulation with Veloce has more than ten times faster than hardware simulation. Our experimental results demonstrate that Mentor Graphics Veloce has major advantages in emulation, verification, and debugging of complicated real hardware designs, especially in the context of SoC complexity. Through our three major tasks, we will demonstrate that (1) Veloce can successfully emulate large-scale SoC designs; (2) it has much better performance comparing to the state-of-the-art simulation tools; (3) it can significantly accelerate the process of hardware verification and debugging while maintaining full signal visibility.
Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models
Venue: arXiv cs.SI 2016
Year: 2016

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.
Layering of Communication Networks and a Forward-Backward Duality
Venue: arXiv cs.IT 2016
Year: 2016

In layered communication networks there are only connections between intermediate nodes in adjacent layers. Applying network coding to such networks provides a number of benefits in theory as well as in practice. We propose a "layering procedure" to transform an arbitrary network into a layered structure. Furthermore, we derive a "forward-backward duality" for linear network codes, which can be seen as an analogon to the "uplink-downlink duality" in MIMO communication systems.
Design of false color palettes for grayscale reproduction
Venue: arXiv cs.GR 2016
Year: 2016

Design of false color palette is quite easy but some effort has to be done to achieve good dynamic range, contrast and overall appearance of the palette. Such palettes, for instance, are commonly used in scientific papers for presenting the data. However, to lower the cost of the paper most scientists decide to let the data to be printed in grayscale. The same applies to e-book readers based on e-ink where most of them are still grayscale. For majority of false color palettes reproducing them in grayscale results in ambiguous mapping of the colors and may be misleading for the reader. In this article design of false color palettes suitable for grayscale reproduction is described. Due to the monotonic change of luminance of these palettes grayscale representation is very similar to the data directly presented with a grayscale palette. Some suggestions and examples how to design such palettes are provided.
Exploring Dynamic Environments Using Stochastic Search Strategies
Venue: arXiv cs.RO 2016
Year: 2016

In this paper, we conduct a literature review of laws of motion based on stochastic search strategies which are mainly focused on exploring highly dynamic environments. In this regard, stochastic search strategies represent an interesting alternative to cope with uncertainty and reduced perceptual capabilities. This study aims to present an introductory overview of research in terms of directional rules and searching methods mainly based on bio-inspired approaches. This study critically examines the role of animal searching behavior applied to random walk models using stochastic rules and kinesis or taxis. The aim of this study is to examine existing techniques and to select relevant work on random walks and analyze their actual contributions. In this regard, we cover a wide range of displacement events with an orientation mechanism given by a reactive behavior or a source-seeking behavior. Finally, we conclude with a discussion concerning the usefulness of using optimal foraging strategies as a reliable methodology.
Coverage and capacity scaling laws in downlink ultra-dense cellular networks
Venue: arXiv cs.NI 2016
Year: 2016

Driven by new types of wireless devices and the proliferation of bandwidth-intensive applications, data traffic and the corresponding network load are increasing dramatically. Network densification has been recognized as a promising and efficient way to provide higher network capacity and enhanced coverage. Most prior work on performance analysis of ultra-dense networks (UDNs) has focused on random spatial deployment with idealized singular path loss models and Rayleigh fading. In this paper, we consider a more precise and general model, which incorporates multi-slope path loss and general fading distributions. We derive the tail behavior and scaling laws for the coverage probability and the capacity considering strongest base station association in a Poisson field network. Our analytical results identify the regimes in which the signal-to-interference-plus-noise ratio (SINR) either asymptotically grows, saturates, or decreases with increasing network density. We establish general results on when UDNs lead to worse or even zero SINR coverage and capacity, and we provide crisp insights on the fundamental limits of wireless network densification.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Venue: arXiv cs.CV 2016
Year: 2016

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.
Energy Management in Heterogeneous Networks with Cell Activation, User Association and Interference Coordination
Venue: arXiv cs.IT 2016
Year: 2016

The densification and expansion of wireless network pose new challenges on interference management and reducing energy consumption. This paper studies energy-efficient resource management in heterogeneous networks by jointly optimizing cell activation, user association and multicell multiuser channel assignment, according to the long-term average traffic and channel conditions. The proposed framework is built on characterizing the interference coupling by pre-defined interference patterns, and performing resource allocation among these patterns. In this way, the interference fluctuation caused by (de)activating cells is explicitly taken into account when calculating the user achievable rates. A tailored algorithm is developed to solve the formulated problem in the dual domain by exploiting the problem structure, which gives a significant complexity saving. Numerical results show a huge improvement in energy saving achieved by the proposed scheme. The user association derived from the proposed joint resource optimization is mapped to standard-compliant cell selection biasing. This mapping reveals that the cell-specific biasing for energy saving is quite different from that for load balancing investigated in the literature.
Duality between erasures and defects
Venue: arXiv cs.IT 2016
Year: 2016

We investigate the duality of the binary erasure channel (BEC) and the binary defect channel (BDC). This duality holds for channel capacities, capacity achieving schemes, minimum distances, and upper bounds on the probability of failure to retrieve the original message. In addition, the relations between BEC, BDC, binary erasure quantization (BEQ), and write-once memory (WOM) are described. From these relations we claim that the capacity of the BDC can be achieved by Reed-Muller (RM) codes under maximum a posterior (MAP) decoding. Also, polar codes with a successive cancellation encoder achieve the capacity of the BDC. Inspired by the duality between the BEC and the BDC, we introduce locally rewritable codes (LWC) for resistive memories, which are the counterparts of locally repairable codes (LRC) for distributed storage systems. The proposed LWC can improve endurance limit and power efficiency of resistive memories.
Guessing Numbers of Odd Cycles
Venue: arXiv cs.IT 2016
Year: 2016

For a given number of colours, $s$, the guessing number of a graph is the base $s$ logarithm of the size of the largest family of colourings of the vertex set of the graph such that the colour of each vertex can be determined from the colours of the vertices in its neighbourhood. An upper bound for the guessing number of the $n$-vertex cycle graph $C_n$ is $n/2$. It is known that the guessing number equals $n/2$ whenever $n$ is even or $s$ is a perfect square \cite{Christofides2011guessing}. We show that, for any given integer $s\geq 2$, if $a$ is the largest factor of $s$ less than or equal to $\sqrt{s}$, for sufficiently large odd $n$, the guessing number of $C_n$ with $s$ colours is $(n-1)/2 + \log_s(a)$. This answers a question posed by Christofides and Markström in 2011 \cite{Christofides2011guessing}. We also present an explicit protocol which achieves this bound for every $n$. Linking this to index coding with side information, we deduce that the information defect of $C_n$ with $s$ colours is $(n+1)/2 - \log_s(a)$ for sufficiently large odd $n$. Our results are a generalisation of the $s=2$ case which was proven in \cite{bar2011index}.
Distributed Programming via Safe Closure Passing
Venue: arXiv cs.PL 2016
Year: 2016

Programming systems incorporating aspects of functional programming, e.g., higher-order functions, are becoming increasingly popular for large-scale distributed programming. New frameworks such as Apache Spark leverage functional techniques to provide high-level, declarative APIs for in-memory data analytics, often outperforming traditional "big data" frameworks like Hadoop MapReduce. However, widely-used programming models remain rather ad-hoc; aspects such as implementation trade-offs, static typing, and semantics are not yet well-understood. We present a new asynchronous programming model that has at its core several principles facilitating functional processing of distributed data. The emphasis of our model is on simplicity, performance, and expressiveness. The primary means of communication is by passing functions (closures) to distributed, immutable data. To ensure safe and efficient distribution of closures, our model leverages both syntactic and type-based restrictions. We report on a prototype implementation in Scala. Finally, we present preliminary experimental results evaluating the performance impact of a static, type-based optimization of serialization.
Access Control Encryption: Enforcing Information Flow with Cryptography
Venue: arXiv cs.CR 2016
Year: 2016

We initiate the study of Access Control Encryption (ACE), a novel cryptographic primitive that allows fine-grained access control, by giving different rights to different users not only in terms of which messages they are allowed to receive, but also which messages they are allowed to send. Classical examples of security policies for information flow are the well known Bell-Lapadula [BL73] or Biba [Bib75] model: in a nutshell, the Bell-Lapadula model assigns roles to every user in the system (e.g., public, secret and top-secret). A users' role specifies which messages the user is allowed to receive (i.e., the no read-up rule, meaning that users with public clearance should not be able to read messages marked as secret or top-secret) but also which messages the user is allowed to send (i.e., the no write-down rule, meaning that a user with top-secret clearance should not be able to write messages marked as secret or public). To the best of our knowledge, no existing cryptographic primitive allows for even this simple form of access control, since no existing cryptographic primitive enforces any restriction on what kind of messages one should be able to encrypt. Our contributions are: - Introducing and formally defining access control encryption (ACE); - A construction of ACE with complexity linear in the number of the roles based on classic number theoretic assumptions (DDH, Paillier); - A construction of ACE with complexity polylogarithmic in the number of roles based on recent results on cryptographic obfuscation;
Modeling Cellular Networks in Fading Environments with Dominant Specular Components
Venue: arXiv cs.IT 2016
Year: 2016

Stochastic geometry (SG) has been widely accepted as a fundamental tool for modeling and analyzing cellular networks. However, the fading models used with SG analysis are mainly confined to the simplistic Rayleigh fading, which is extended to the Nakagami-m fading in some special cases. However, neither the Rayleigh nor the Nakagami-m accounts for dominant specular components (DSCs) which may appear in realistic fading channels. In this paper, we present a tractable model for cellular networks with generalized two-ray (GTR) fading channel. The GTR fading explicitly accounts for two DSCs in addition to the diffuse components and offers high flexibility to capture diverse fading channels that appear in realistic outdoor/indoor wireless communication scenarios. It also encompasses the famous Rayleigh and Rician fading as special cases. To this end, the prominent effect of DSCs is highlighted in terms of average spectral efficiency.
HDBSCAN: Density based Clustering over Location Based Services
Venue: arXiv cs.DB 2016
Year: 2016

Location Based Services (LBS) have become extremely popular and used by millions of users. Popular LBS run the entire gamut from mapping services (such as Google Maps) to restaurants (such as Yelp) and real-estate (such as Redfin). The public query interfaces of LBS can be abstractly modeled as a kNN interface over a database of two dimensional points: given an arbitrary query point, the system returns the k points in the database that are nearest to the query point. Often, k is set to a small value such as 20 or 50. In this paper, we consider the novel problem of enabling density based clustering over an LBS with only a limited, kNN query interface. Due to the query rate limits imposed by LBS, even retrieving every tuple once is infeasible. Hence, we seek to construct a cluster assignment function f(.) by issuing a small number of kNN queries, such that for any given tuple t in the database which may or may not have been accessed, f(.) outputs the cluster assignment of t with high accuracy. We conduct a comprehensive set of experiments over benchmark datasets and popular real-world LBS such as Yahoo! Flickr, Zillow, Redfin and Google Maps.
Qualities and Inequalities in Online Social Networks through the Lens of the Generalized Friendship Paradox
Venue: arXiv cs.SI 2016
Year: 2016

The friendship paradox is the phenomenon that in social networks, people on average have fewer friends than their friends do. The generalized friendship paradox is an extension to attributes other than the number of friends. The friendship paradox and its generalized version have gathered recent attention due to the information they provide about network structure and local inequalities. In this paper, we propose several measures of nodal qualities which capture different aspects of their activities and influence in online social networks. Using these measures we analyse the prevalence of the generalized friendship paradox over Twitter and we report high levels of prevalence (up to over 90\% of nodes). We contend that this prevalence of the friendship paradox and its generalized version arise because of the hierarchical nature of the connections in the network. This hierarchy is nested as opposed to being star-like. We conclude that these paradoxes are collective phenomena not created merely by a minority of well-connected or high-attribute nodes. Moreover, our results show that a large fraction of individuals can experience the generalized friendship paradox even in the absence of a significant correlation between degrees and attributes.
Query By Provenance
Venue: arXiv cs.DB 2016
Year: 2016

To assist non-specialists in formulating database queries, multiple frameworks that automatically infer queries from a set of examples have been proposed. While highly useful, a shortcoming of the approach is that if users can only provide a small set of examples, many inherently different queries may qualify, and only some of these actually match the user intentions. Our main observation is that if users further explain their examples, the set of qualifying queries may be significantly more focused. We develop a novel framework where users explain example tuples by choosing input tuples that are intuitively the "cause" for their examples. Their explanations are automatically "compiled" into a formal model for explanations, based on previously developed models of data provenance. Then, our novel algorithms infer conjunctive queries from the examples and their explanations. We prove the computational efficiency of the algorithms and favorable properties of inferred queries. We have further implemented our solution in a system prototype with an interface that assists users in formulating explanations in an intuitive way. Our experimental results, including a user study as well as experiments using the TPC-H benchmark, indicate the effectiveness of our solution.
Parallel Shortest-Paths Using Radius Stepping
Venue: arXiv cs.DS 2016
Year: 2016

The single-source shortest path problem (SSSP) with nonnegative edge weights is a notoriously difficult problem to solve efficiently in parallel---it is one of the graph problems said to suffer from the transitive-closure bottleneck. In practice, the $Δ$-stepping algorithm of Meyer and Sanders (J. Algorithms, 2003) often works efficiently but has no known theoretical bounds on general graphs. The algorithm takes a sequence of steps, each increasing the radius by a user-specified value $Δ$. Each step settles the vertices in its annulus but can take $Θ(n)$ substeps, each requiring $Θ(m)$ work ($n$ vertices and $m$ edges). In this paper, we describe Radius-Stepping, an algorithm with the best-known tradeoff between work and depth bounds for SSSP with nearly-linear ($\otilde(m)$) work. The algorithm is a $Δ$-stepping-like algorithm but uses a variable instead of fixed-size increase in radii, allowing us to prove a bound on the number of steps. In particular, by using what we define as a vertex $k$-radius, each step takes at most $k+2$ substeps. Furthermore, we define a $(k, ρ)$-graph property and show that if an undirected graph has this property, then the number of steps can be bounded by $O(\frac{n}ρ \log ρL)$, for a total of $O(\frac{kn}ρ \log ρL)$ substeps, each parallel. We describe how to preprocess a graph to have this property. Altogether, Radius-Stepping takes $O((m+n\log n)\log \frac{n}ρ)$ work and $O(\frac{n}ρ\log n \log (ρL))$ depth per source after preprocessing. The preprocessing step can be done in $O(m\log n + nρ^2)$ work and $O(ρ^2)$ depth or in $O(m\log n + nρ^2\log n)$ work and $O(ρ\log ρ)$ depth, and adds no more than $O(nρ)$ edges.